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Run MLOps on Kubernetes: train with Kubeflow Trainer, serve with KServe, schedule GPUs with Kueue, autoscale with KEDA, and ship via GitOps.
Canonical has announced the general availability of Managed Kubeflow on the Microsoft Azure Marketplace. This fully managed MLOps platform allows enterprise AI teams to deploy a pr...
PARTNER CONTENT: Why platform teams are swapping DIY Kubeflow for Canonical's managed service
Helmfile может координировать несколько Helm релизов. Argo CD и Flux могут синхронизировать Kubernetes объекты с Git. Terraform и Pulumi могут создать инфраструктуру. Argo Workflow...
Part 2 of an MLOps End-to-End series — 60 models, fully automated, one Airflow DAGContinue reading on Medium »
The top 11 MLOps tools for 2026, MLflow, Kubeflow, SageMaker, Vertex AI, and more, compared by features, pricing, and best-fit use cases.
IntroductionContinue reading on Medium »
A hands-on 2026 guide to deploying ML models with Docker and Kubernetes: containerize a FastAPI service, run it on a cluster, and autoscale it.
A practical walkthrough of building and deploying a multistage, multimodal recommender system on Amazon EKS, covering data pipelines, model training, Bloom filters, feature caching...
Build a complete MLOps pipeline in 90 minutes with MLflow 3, DVC, FastAPI, and Docker. Hands-on tutorial with working code and monitoring.
Привет, Хабр! Меня зовут Антон Алексеев, я MLOps-инженер в Авито. В статье рассказываю, как мы строим ML-платформу на базе Kubeflow. От первых DevBox-решений мы пришли к набору неб...
Modern data engineering rarely lives on a single machine. As datasets grow from gigabytes into terabytes — and sometimes into petabytes — teams need orchestration tools that can sc...
A hands-on Kubernetes tutorial for beginners: deploy your first app, expose and scale it, see self-healing, and use YAML with kubectl.
What happens when your workload fails in one region but you need access to service? This is a common case for availability and uptime. With recent enhancement to the Kubernetes eco...
Raw data doesn't win model competitions. Features do. And when your raw data is tens of billions of rows sitting across multiple sources, you can't afford to run pandas in a notebo...
Most teams adopt Kubernetes for the runtime benefits — self-healing pods, horizontal scaling, declarative configuration — but then bolt on a CI/CD tool as an afterthought. The resu...
Google Kubernetes Engine (GKE) managed DRANET supports both GPUs and TPUs. There are several configurations to use this implementation, including standard cluster (where you have f...
Automate ML retraining with GitHub Actions and Jenkins: triggers, schedules, self-hosted GPU runners, and a quality gate that blocks bad models.
Why Fine-Tune on Databricks? General-purpose LLMs like Llama 3, Mistral, or Falcon are impressive out of the box — but they underperform on domain-specific tasks: medical coding, l...
I thought deploying an LLM was just like deploying a microservice. I was wrong in ways that took three production incidents to fully…Continue reading on Medium »
Master Kubernetes interviews with top questions and answers on important topics with KodeKlode.
If you run GPU workloads on Kubernetes — vLLM, Triton, training jobs, or the newer agentic inference stacks — you’ve probably hit a familiar problem: the default autoscaling path s...
Start tuning and serving AI models on a single DGX Spark with ready-made templates, then scale the same workflows to
Google created MapReduce more than 20 years ago to solve the scaling problems in data processing that the then young company was running into. The AI era that we are in now demands...
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